Time series aggregation for energy system design: Modeling seasonal storage

TL;DR

This paper introduces a new mathematical approach for modeling seasonal storage in energy systems, enabling more accurate and computationally efficient optimization of renewable energy configurations with high seasonal variability.

Contribution

It proposes a novel superposition-based storage inventory model that captures seasonal storage dynamics within aggregated time series for energy system design.

Findings

Significant reduction in computational load for long-term storage models

Effective modeling of seasonal storage with aggregated typical periods

Applicable to various energy system configurations

Abstract

The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation. To address this issue, this paper proposes a novel mathematical description for storage inventories based on the superposition of inter-period and intra-period states. Inter-period states connect the typical periods and are able to account their sequence. The approach has been adopted for different energy system configurations. The results show that a significant reduction in the computational load can be achieved also for long term storage-based energy system models in comparison to optimization models based on the full annual time series.